---
title: "Glossary"
metatags:
  description: "Hardware concepts, communication libraries, deployment terminology, and common abbreviations for deploying SGLang on Ascend NPUs."
---

This page covers the hardware concepts, communication libraries, deployment terminology, and common abbreviations
you will encounter throughout the Ascend NPU documentation. Refer back here when you run into unfamiliar terms.

## Hardware

### Supported devices

SGLang supports the following Ascend inference hardware:

| Hardware           | Chip        | Devices | Dies per card | Memory configuration                 |
| ------------------ | ----------- | ------- | ------------- | ------------------------------------ |
| Atlas 800I A2 (A2) | Ascend 910B | 8       | 1             | 8(cards) × 1(die/card) × 64(GB/die)  |
| Atlas 800I A3 (A3) | Ascend 910C | 16      | 2             | 8(cards) × 2(dies/card) × 64(GB/die) |

<Note>
Throughout these docs, **A2** and **A3** are used as shorthand for the hardware above.
Docker image tags use `910b` for A2 and `a3` for A3.
For example, `cann9.0.0-910b-v0.5.16` and `cann9.0.0-a3-v0.5.16`.

On A3, each card has 2 dies, giving 16 devices vs 8 on A2.
Benchmark pages use "Cards" to refer to physical cards, so `Cards: 4` on A3 means `--tp-size 8`.

From a deployment perspective, the two key differences between A2 and A3 are:

1. dies per card — which drives both total memory and `--tp-size` configuration
2. PD disaggregation — A2 requires setting `export ASCEND_MF_TRANSFER_PROTOCOL="device_rdma"`, while A3 uses the default protocol.
</Note>

### NPU

**NPU** stands for Neural Processing Unit. Each NPU device is a single `davinci` core.
The terms "NPU" and "davinci" are used interchangeably in commands and error logs.

On A2, devices are numbered `/dev/davinci0` through `/dev/davinci7` (8 devices).
On A3, devices are numbered `/dev/davinci0` through `/dev/davinci15` (16 devices).

On either an A2 or A3 server, run `npu-smi info` to view NPU information such as device
health, memory usage, and chip status. If the command is not found or reports no devices,
the driver is likely not installed. Follow the
[Ascend driver installation guide](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/900/softwareinst/instg/instg_0005.html?OS=openEuler&InstallType=local)
to install it.

## Communication libraries

| Library                                            | Description                                                                                                                                                               |
| -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **HCCL** (Huawei Collective Communication Library) | The primary communication backend for multi-card data transfer on Ascend NPUs. Equivalent to NVIDIA NCCL. Used in `--nnodes`, `--tp-size`, and all distributed scenarios. |
| **GLOO**                                           | Meta's collective communications library. Used alongside HCCL for distributed initialization and coordination.                                                            |
| **DeepEP** (Deep Expert Parallelism)               | A communication library optimized for Mixture-of-Experts (MoE) all-to-all dispatch and combine operations. Used with `--moe-a2a-backend deepep`.                          |
| **RDMA** (Remote Direct Memory Access)             | Enables direct memory access between nodes over InfiniBand or RoCE networks. Required for multi-node PD disaggregation.                                                   |

## Quantization and precision

| Notation  | Meaning                                                            |
| --------- | ------------------------------------------------------------------ |
| **W8A8**  | 8-bit weights, 8-bit activations                                   |
| **W4A8**  | 4-bit weights, 8-bit activations                                   |
| **W4A16** | 4-bit weights, 16-bit activations                                  |
| **BF16**  | Brain Floating Point 16 — 16-bit format optimized for ML workloads |
| **FP8**   | 8-bit Floating Point — not supported on A2/A3                       |
| **INT8**  | 8-bit Integer quantization                                         |

To apply quantization, use `--quantization modelslim` for W8A8 INT8, or load a pre-quantized
checkpoint directly from a model hub.

## Deployment terminology

### Prefill-Decode (PD) disaggregation

PD disaggregation separates inference into two stages running on different nodes:

- **Prefill** (P): Processes the entire input prompt at once. Compute-bound.
- **Decode** (D): Generates tokens one at a time. Memory-bandwidth-bound.

**PD Mixed**: Both stages run on the same set of nodes.

| Shorthand | Meaning                         |
| --------- | ------------------------------- |
| **1P1D**  | 1 prefill node + 1 decode node  |
| **2P1D**  | 2 prefill nodes + 1 decode node |
| **1P2D**  | 1 prefill node + 2 decode nodes |

You will see these in [Best Practice](/docs/hardware-platforms/ascend-npus/model-deployment/best-practices/deepseek_r1)
section headings, e.g., `W8A8 2P1D 32P` means "W8A8 quantization, 2 prefill nodes +
1 decode node, 32 cards total."

### Parallelism strategies

| Strategy                      | Flag             | Description                                                             |
| ----------------------------- | ---------------- | ----------------------------------------------------------------------- |
| **Tensor Parallelism (TP)**   | `--tp-size`      | Splits model weights across NPUs within a node                          |
| **Data Parallelism (DP)**     | `--dp-size`      | Replicates the model across nodes for higher throughput                 |
| **Expert Parallelism (EP)**   | `--ep-size`      | Distributes MoE experts across devices; requires `--moe-a2a-backend`    |
| **Context Parallelism (CP)**  | `--attn-cp-size` | Splits long context windows across devices for extended sequence length |
| **Pipeline Parallelism (PP)** | `--pp-size`      | Splits model layers across devices sequentially                         |

### Speculative decoding

| Algorithm                        | Description                                                                                                                                  |
| -------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| **EAGLE3**                       | Uses an external draft model (specified via `--speculative-draft-model-path`) to propose candidate tokens. Supports top-k sampling.          |
| **NEXTN**                        | Uses the model's built-in Multi-Token Prediction (MTP) heads — no separate draft model needed. Available for models with native MTP support. |
| **MTP** (Multi-Token Prediction) | A model architecture feature where the model predicts multiple future tokens per step. The foundation for NEXTN speculative decoding.        |

### Performance metrics

| Metric                            | Description                                                                               |
| --------------------------------- | ----------------------------------------------------------------------------------------- |
| **TPOT** (Time Per Output Token)  | Average time to generate each output token. Lower is better.                              |
| **TTFT** (Time To First Token)    | Latency from request arrival to first generated token. Critical for interactive use.      |

## Model architecture terms

| Term                                  | Description                                                                                                                                              |
| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **MoE** (Mixture of Experts)          | Model architecture where only a subset of parameters (experts) is activated per token, reducing compute. Common in DeepSeek, Qwen3-30B-A3B, and MiMo models. |
| **MLA** (Multi-head Latent Attention) | Attention variant that compresses key-value representations into a lower-dimensional latent space. Used by DeepSeek models.                              |
| **GQA** (Grouped-Query Attention)     | Attention variant where multiple query heads share a single key-value head. Used by Qwen3 dense models.                                                  |
| **DSA** (DeepSeek Sparse Attention)   | DeepSeek's sparse attention mechanism; reduces KV cache overhead for long contexts.                                                                      |
| **FFN** (Feed-Forward Network)        | The non-attention component of each transformer layer. In MoE models, the FFN is replaced by multiple expert FFN layers selected by a router.            |

## Other common abbreviations

| Abbreviation                                     | Expansion                                                               |
| ------------------------------------------------ | ----------------------------------------------------------------------- |
| **ACL** (Ascend Computing Language)              | Low-level Ascend compute API; seen in error logs                        |
| **DVFS** (Dynamic Voltage and Frequency Scaling) | Hardware frequency scaling to maintain performance stability            |
| **NUMA** (Non-Uniform Memory Access)             | Memory architecture affecting multi-socket performance                  |
| **KV Cache** (Key-Value Cache)                   | Cached attention key-value tensors to avoid recomputation during decode |
| **LoRA** (Low-Rank Adaptation)                   | Parameter-efficient fine-tuning method                                  |
| **HF** (Hugging Face)                            | Model hub; `HF_TOKEN` / `HF_ENDPOINT` control model download access     |
| **UB** (Unified Buffer)                          | On-chip NPU memory; referenced in operator optimization                 |
| **DMA** (Direct Memory Access)                   | Data transfer mechanism between host and device memory                  |

## Where to go next

- [Quickstart](/docs/hardware-platforms/ascend-npus/getting-started/quick_start) — launch your first server
- [Installation Guide](/docs/hardware-platforms/ascend-npus/getting-started/installation) — full installation with component version mapping
- [Supported Features](/docs/hardware-platforms/ascend-npus/reference/support_features) — per-parameter Ascend support status
- [Supported Models](/docs/hardware-platforms/ascend-npus/reference/support_models) — models verified on Ascend NPU
- [Model Tutorials](/docs/hardware-platforms/ascend-npus/model-deployment/tutorials/deepseek_r1) — step-by-step deployment guides
- [Best Practice](/docs/hardware-platforms/ascend-npus/model-deployment/best-practices/deepseek_r1) — benchmark configurations and results
